Three-dimensional foam flow resolved by fast X-ray tomographic microscopy

Abstract

Thanks to ultra fast and high resolution X-ray tomography, we managed to capture the evolution of the local structure of the bubble network of a 3D foam flowing around a sphere. As for the 2D foam flow around a circular obstacle, we observed an axisymmetric velocity field with a recirculation zone, and indications of a negative wake downstream the obstacle. The bubble deformations, quantified by a shape tensor, are smaller than in 2D, due to a purely 3D feature: the azimuthal bubble shape variation. Moreover, we were able to detect plastic rearrangements, characterized by the neighborswapping of four bubbles. Their spatial structure suggest that rearrangements are triggered when films faces get smaller than a characteristic area.

Flow of foam through a convergent channel

“…The velocity profile (3) shows that wall friction is responsible for a deviation from (1). It also captures the fact that the velocity at the side walls is lower than at the centre of the channel, because of the extra friction at the side walls.…”

“…Because foams are opaque in bulk, obtaining the information at the bubble scale is difficult on three-dimensional (3D) foams and the local description of 3D foam flows has been performed only very recently, using fast X-ray tomography [3]. Most experimental studies have resorted to foams confined in two dimensions (2D), e.g.…”

Four-Dimensional Tomographic Reconstruction by Time Domain Decomposition

“…In the following we show the reconstruction of experimental data. We investigate the rheology of liquid foams by fast synchrotron X-ray tomographic microscopy [42]. Foams are complex cellular systems which require artifact free tomographic reconstruction for a reliable quantification of their time-dependent properties such as deformation fields of bubbles.…”

GigaFRoST: the gigabit fast readout system for tomography

“…X-ray tomographic microscopy already helped to reveal part of the physics (Lambert et al, 2010) which could be earlier addressed only on reduced dimensions (2D) (Dollet & Graner, 2007). Recently, liquid foam rheology in 3D was better understood (Raufaste et al, 2015), but in the same study it was pointed out that the statistics and hence the confidence in the results were significantly affected by the severe limitations on the time span of one sequence acquisition. In the following example using the GigaFRoST detector, we could for the first time capture a foam rheology study with high spatio-temporal resolution and at the same time a sufficiently long total acquisition of the dynamic series.…”